{smcl}
{txt}{sf}{ul off}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}/N/project/suicide_study/pnas_replication/results/log/logit_3.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}18 Aug 2020, 07:18:48
{txt}
{com}. 
. if ("`model'" == "logit"){c -(}
.         use "${c -(}home_dir{c )-}/data/processed/suicide_reg_v1_raw.dta", clear
. {c )-}
{txt}
{com}. 
. if ("`model'" == "mi") {c -(}
.         use "${c -(}home_dir{c )-}/data/processed/suicide_reg_v1_imputed_M10.dta", clear  
. {c )-}
{txt}
{com}. 
. * for now, we use the following simple survey weights 
. if ("`model'" == "mi") {c -(}
.         mi svyset `geo_type' [pw=ObsWgt0] 
. {c )-}
{txt}
{com}. else {c -(}
.         svyset `geo_type' [pw=ObsWgt0]  

      {txt}pweight:{col 16}{res}ObsWgt0
          {txt}VCE:{col 16}{res}linearized
  {txt}Single unit:{col 16}{res}missing
     {txt}Strata 1:{col 16}<one>
         SU 1:{col 16}{res}county
        {txt}FPC 1:{col 16}<zero>
{p2colreset}{...}
{com}. {c )-}
{txt}
{com}. 
. * margins for each category
. program margin_interact 
{txt}  1{com}.         args X Y k model
{txt}  2{com}.         sum `X', d 
{txt}  3{com}.         local gap = (`r(max)' - `r(min)') / `k' 
{txt}  4{com}.         if ("`model'" == "logit") {c -(}
{txt}  5{com}.                 margin `Y', at(`X' = (`r(min)' (`gap') `r(max)')) predict(pr)
{txt}  6{com}.         {c )-} 
{txt}  7{com}.         else if ("`model'" == "mi") {c -(}
{txt}  8{com}.                 mimrgns `Y', at(`X' = (`r(min)' (`gap') `r(max)')) predict(pr)
{txt}  9{com}.         {c )-}       
{txt} 10{com}. end 
{txt}
{com}. 
. program mchange_mi
{txt}  1{com}.         args X k model
{txt}  2{com}.         if ("`model'" == "logit") {c -(}
{txt}  3{com}. 
.                 if ("`k'" == "continuous") {c -(}
{txt}  4{com}.                         sum `X' if e(sample), d 
{txt}  5{com}.                         margin, at(`X' = (`r(min)' `r(max)')) post predict(pr)
{txt}  6{com}.                         mlincom  2 - 1, decimal(7) stat(all)    
{txt}  7{com}.                 {c )-} 
{txt}  8{com}.                 else if ("`k'" == "binary") {c -(}
{txt}  9{com}.                         sum `X' if e(sample), d 
{txt} 10{com}.                         margin, at(`X' = (`r(min)' `r(max)')) post predict(pr)
{txt} 11{com}.                         mlincom  2 - 1, decimal(7) stat(all)
{txt} 12{com}.                 {c )-} 
{txt} 13{com}.                 else if ("`k'" == "categorical") {c -(}
{txt} 14{com}.                         margin `X' if e(sample)==1 , at() pwcompare predict(pr) 
{txt} 15{com}.                 {c )-}
{txt} 16{com}.         {c )-}
{txt} 17{com}. 
.         else if ("`model'" == "mi") {c -(}
{txt} 18{com}. 
.                 if ("`k'" == "continuous") {c -(}
{txt} 19{com}.                         sum `X' , d 
{txt} 20{com}.                         mimrgns, at(`X' = (`r(min)' `r(max)')) post predict(pr)
{txt} 21{com}.                         mlincom  2 - 1, decimal(7) stat(all)
{txt} 22{com}.                 {c )-} 
{txt} 23{com}.                 else if ("`k'" == "binary") {c -(}
{txt} 24{com}.                         sum `X' , d 
{txt} 25{com}.                         mimrgns, at(`X' = (`r(min)' `r(max)')) post predict(pr)
{txt} 26{com}.                         mlincom  2 - 1, decimal(7) stat(all)
{txt} 27{com}.                 {c )-} 
{txt} 28{com}.                 else if ("`k'" == "categorical") {c -(}
{txt} 29{com}.                         mimrgns `X' , at()   pwcompare predict(pr)      
{txt} 30{com}.                 {c )-}
{txt} 31{com}.         {c )-}
{txt} 32{com}. end
{txt}
{com}. 
. * create some dummy codings
. tab Race5, gen(race5_nh)

      {txt}Race5 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}  9,802,895       79.53       79.53
{txt}          2 {c |}{res}  1,330,104       10.79       90.32
{txt}          3 {c |}{res}    260,664        2.11       92.44
{txt}          4 {c |}{res}    250,301        2.03       94.47
{txt}          5 {c |}{res}    681,739        5.53      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res} 12,325,703      100.00
{txt}
{com}.         rename race5_nh1 White_nh 
{res}{txt}
{com}.         rename race5_nh2 Black_nh 
{res}{txt}
{com}.         rename race5_nh3 AIAN_nh 
{res}{txt}
{com}.         rename race5_nh4 AsPI_nh 
{res}{txt}
{com}.         rename race5_nh5 Hispanic
{res}{txt}
{com}. 
. tab MarStat5, gen(ms)

   {txt}MarStat5 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}  6,880,735       55.83       55.83
{txt}          2 {c |}{res}    924,215        7.50       63.32
{txt}          3 {c |}{res}  1,265,861       10.27       73.60
{txt}          4 {c |}{res}    237,203        1.92       75.52
{txt}          5 {c |}{res}  3,017,228       24.48      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res} 12,325,242      100.00
{txt}
{com}.         rename ms1 Marrd5
{res}{txt}
{com}.         rename ms2 Widow5
{res}{txt}
{com}.         rename ms3 Divor5
{res}{txt}
{com}.         rename ms4 Separ5
{res}{txt}
{com}.         rename ms5 NvMar5
{res}{txt}
{com}. 
. tab AgeGrp4, gen(ag) 

    {txt}AgeGrp4 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}  1,930,142       15.66       15.66
{txt}          2 {c |}{res}  3,549,392       28.80       44.46
{txt}          3 {c |}{res}  4,304,660       34.92       79.38
{txt}          4 {c |}{res}  2,541,509       20.62      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res} 12,325,703      100.00
{txt}
{com}.         rename ag1 Age_15_24
{res}{txt}
{com}.         rename ag2 Age_25_44
{res}{txt}
{com}.         rename ag3 Age_45_64
{res}{txt}
{com}.         rename ag4 Age_65_Up
{res}{txt}
{com}. 
. destring St, replace 
{txt}St: all characters numeric; {res}replaced {txt}as {res}byte
{txt}(16685 missing values generated)
{res}{txt}
{com}. 
. * set-up equations
. local religion Rat_GC_ProE Rat_GC_ProM Rat_GC_ProB Rat_GC_Cath Rat_GC_Jew Rat_GC_Oth
{txt}
{com}. local contextual_control Rat_Poverty Rat_Mig_Cum Pop_Den
{txt}
{com}. 
. local religion Rat_GC_ProE Rat_GC_ProM Rat_GC_ProB Rat_GC_Jew Rat_GC_Oth
{txt}
{com}. local contextual_control Rat_Poverty Rat_Mig_Cum Pop_Den
{txt}
{com}. 
. local demographics_raw i.Female c.RAT_Female i.AgeGrp4 c.RAT_AgeGrp4_2 c.RAT_AgeGrp4_3 c.RAT_AgeGrp4_4 i.Race5 c.RAT_Race5_2 c.RAT_Race5_3 c.RAT_Race5_4 c.RAT_Race5_5 i.BornUSA c.RAT_BornUSA i.MarStat5 c.RAT_MarStat5_2 c.RAT_MarStat5_3 c.RAT_MarStat5_4 c.RAT_MarStat5_5
{txt}
{com}. local demographics_same i.Female c.std_same_prop_Sex i.AgeGrp4 c.std_same_prop_AgeGrp4 i.Race5 c.std_same_prop_Race5 i.BornUSA c.std_same_prop_BornUSA i.MarStat5 c.std_same_prop_MarStat5 
{txt}
{com}. local demographics_inter i.Female##c.std_same_prop_Sex i.AgeGrp4##c.std_same_prop_AgeGrp4 i.Race5##c.std_same_prop_Race5 i.BornUSA##c.std_same_prop_BornUSA i.MarStat5##c.std_same_prop_MarStat5
{txt}
{com}. 
. 
. if (`model_version' == 1){c -(}
.         local model_eq i.Year `demographics_raw' `contextual_control' `religion' 
.         local margin_demographics Female RAT_Female AgeGrp4 RAT_AgeGrp4_2 RAT_AgeGrp4_3 RAT_AgeGrp4_4 Race5 RAT_Race5_2 RAT_Race5_3 RAT_Race5_4 RAT_Race5_5 BornUSA RAT_BornUSA MarStat5 RAT_MarStat5_2 RAT_MarStat5_3 RAT_MarStat5_4 RAT_MarStat5_5 
. {c )-}
{txt}
{com}. if (`model_version' == 2){c -(}
.         local model_eq i.Year `demographics_raw' `contextual_control' `religion' i.UnEmpl c.RAT_UnEmpl i.PhysProb c.RAT_PhysProb
.         local margin_demographics Female RAT_Female AgeGrp4 RAT_AgeGrp4_2 RAT_AgeGrp4_3 RAT_AgeGrp4_4 Race5 RAT_Race5_2 RAT_Race5_3 RAT_Race5_4 RAT_Race5_5 BornUSA RAT_BornUSA MarStat5 RAT_MarStat5_2 RAT_MarStat5_3 RAT_MarStat5_4 RAT_MarStat5_5 UnEmpl RAT_UnEmpl PhysProb RAT_PhysProb
. {c )-}
{txt}
{com}. if (`model_version' == 3){c -(}
.         local model_eq i.Year `demographics_same' `contextual_control' `religion' 
.         local margin_demographics Female std_same_prop_Sex AgeGrp4 std_same_prop_AgeGrp4 Race5 std_same_prop_Race5 BornUSA std_same_prop_BornUSA MarStat5 std_same_prop_MarStat5 
. {c )-}
{txt}
{com}. if (`model_version' == 4){c -(}
.         local model_eq i.Year `demographics_same' `contextual_control' `religion' i.UnEmpl c.std_same_prop_UnEmpl i.PhysProb c.std_same_prop_PhysProb
.         local margin_demographics Female std_same_prop_Sex AgeGrp4 std_same_prop_AgeGrp4 Race5 std_same_prop_Race5 BornUSA std_same_prop_BornUSA MarStat5 std_same_prop_MarStat5 UnEmpl std_same_prop_UnEmpl PhysProb std_same_prop_PhysProb
. {c )-}
{txt}
{com}. if (`model_version' == 5){c -(}
.         local model_eq i.Year `demographics_inter' `contextual_control' `religion' 
. {c )-}
{txt}
{com}. if (`model_version' == 6){c -(}
.         local model_eq i.Year `demographics_inter' `contextual_control' `religion' i.UnEmpl##c.std_same_prop_UnEmpl i.PhysProb##c.std_same_prop_PhysProb
. {c )-}       
{txt}
{com}. * test how long it would take.
. * mi estimate: svy: mean Suic 
. * local demographics i.Female c.RAT_Female i.AgeGrp4 c.RAT_AgeGrp4_2 c.RAT_AgeGrp4_3 c.RAT_AgeGrp4_4 i.Race5 c.RAT_Race5_2 c.RAT_Race5_3 c.RAT_Race5_4 c.RAT_Race5_5 i.BornUSA c.RAT_BornUSA i.MarStat5 c.RAT_MarStat5_2 c.RAT_MarStat5_3 c.RAT_MarStat5_4 c.RAT_MarStat5_5
. * mi estimate: svy: logit Suic i.St `demographics' UnEmpl RAT_UnEmpl PhysProb RAT_PhysProb
. 
. * main effects : margins
. if ("`model'" == "mi"){c -(}
. 
.         mi estimate: svy: logit Suic i.St `model_eq', or 
.         estimates store m1 
. 
.         if (`model_version' <= 4){c -(}
.                 estimates restore m1
.                 mchange_mi Female "binary" "mi"
.                 estimates restore m1
.                 mchange_mi AgeGrp4 "categorical" "mi"
.                 estimates restore m1
.                 mchange_mi Race5 "categorical" "mi"
.                 estimates restore m1
.                 mchange_mi BornUSA "binary" "mi"
.                 estimates restore m1
.                 mchange_mi MarStat5 "categorical" "mi"
.                 
.                 if (`model_version' == 1 | `model_version' == 2) {c -(}
.                         estimates restore m1
.                         mchange_mi RAT_Female "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_AgeGrp4_2 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_AgeGrp4_3 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_AgeGrp4_4 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_Race5_2 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_Race5_3 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_Race5_4 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_Race5_5 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_BornUSA "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_MarStat5_2 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_MarStat5_3 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_MarStat5_4 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_MarStat5_5 "continuous" "mi"
.                 {c )-}
.                 if (`model_version' == 3 | `model_version' == 4) {c -(}
.                         estimates restore m1
.                         mchange_mi std_same_prop_Sex "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi std_same_prop_AgeGrp4 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi std_same_prop_Race5 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi std_same_prop_BornUSA "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi std_same_prop_MarStat5 "continuous" "mi"
.                 {c )-}
.         
.                 if (`model_version' == 2 | `model_version' == 4){c -(}
.                         estimates restore m1
.                         mchange_mi UnEmpl "categorical" "mi"
.                         estimates restore m1
.                         mchange_mi PhysProb "categorical" "mi"
.                 {c )-}
.         
.                 if (`model_version' == 2){c -(}
.                         estimates restore m1
.                         mchange_mi RAT_UnEmpl "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_PhysProb "continuous" "mi"
.                 {c )-}
.                 if (`model_version' == 4){c -(}
.                         estimates restore m1
.                         mchange_mi std_same_prop_UnEmpl "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi std_same_prop_PhysProb "continuous" "mi"
.                 {c )-}               
.         {c )-}
. {c )-}
{txt}
{com}. 
. if ("`model'" == "logit"){c -(}
.         svy: logit Suic i.St `model_eq', or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 47}Number of obs{col 65}= {res}  11,813,849
{txt}{col 1}Number of PSUs{col 20}= {res}      918{txt}{col 47}Population size{col 65}={res}  418,785,985
{txt}{col 47}Design df{col 65}= {res}         917
{txt}{col 47}F({res}  47{txt},{res}    871{txt}){col 65}= {res}      669.03
{txt}{col 47}Prob > F{col 65}= {res}      0.0000

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}        Suic{col 14}{c |} Odds Ratio{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}St {c |}
{space 10}8  {c |}{col 14}{res}{space 2}  .901586{col 26}{space 2} .0874195{col 37}{space 1}   -1.07{col 46}{space 3}0.286{col 54}{space 4} .7453563{col 67}{space 3} 1.090562
{txt}{space 9}13  {c |}{col 14}{res}{space 2} .6237012{col 26}{space 2} .0598287{col 37}{space 1}   -4.92{col 46}{space 3}0.000{col 54}{space 4} .5166743{col 67}{space 3} .7528982
{txt}{space 9}21  {c |}{col 14}{res}{space 2} .5422593{col 26}{space 2} .0540945{col 37}{space 1}   -6.13{col 46}{space 3}0.000{col 54}{space 4}  .445842{col 67}{space 3} .6595278
{txt}{space 9}24  {c |}{col 14}{res}{space 2} .5191597{col 26}{space 2} .0502019{col 37}{space 1}   -6.78{col 46}{space 3}0.000{col 54}{space 4} .4294201{col 67}{space 3} .6276528
{txt}{space 9}25  {c |}{col 14}{res}{space 2} .3955652{col 26}{space 2} .0385462{col 37}{space 1}   -9.52{col 46}{space 3}0.000{col 54}{space 4}   .32671{col 67}{space 3} .4789319
{txt}{space 9}34  {c |}{col 14}{res}{space 2} .4402789{col 26}{space 2} .0435058{col 37}{space 1}   -8.30{col 46}{space 3}0.000{col 54}{space 4} .3626653{col 67}{space 3} .5345025
{txt}{space 9}35  {c |}{col 14}{res}{space 2} 1.033065{col 26}{space 2} .1029791{col 37}{space 1}    0.33{col 46}{space 3}0.744{col 54}{space 4}  .849503{col 67}{space 3}  1.25629
{txt}{space 9}37  {c |}{col 14}{res}{space 2} .6545784{col 26}{space 2} .0654563{col 37}{space 1}   -4.24{col 46}{space 3}0.000{col 54}{space 4} .5379365{col 67}{space 3} .7965119
{txt}{space 9}40  {c |}{col 14}{res}{space 2} .5472149{col 26}{space 2} .0577113{col 37}{space 1}   -5.72{col 46}{space 3}0.000{col 54}{space 4} .4449062{col 67}{space 3} .6730501
{txt}{space 9}41  {c |}{col 14}{res}{space 2} .7512551{col 26}{space 2} .0730253{col 37}{space 1}   -2.94{col 46}{space 3}0.003{col 54}{space 4} .6207798{col 67}{space 3} .9091537
{txt}{space 9}44  {c |}{col 14}{res}{space 2} .4522812{col 26}{space 2} .0437478{col 37}{space 1}   -8.20{col 46}{space 3}0.000{col 54}{space 4} .3740809{col 67}{space 3} .5468291
{txt}{space 9}45  {c |}{col 14}{res}{space 2} .5800024{col 26}{space 2} .0571602{col 37}{space 1}   -5.53{col 46}{space 3}0.000{col 54}{space 4}  .478004{col 67}{space 3} .7037656
{txt}{space 9}49  {c |}{col 14}{res}{space 2} 1.781754{col 26}{space 2} .3113879{col 37}{space 1}    3.31{col 46}{space 3}0.001{col 54}{space 4} 1.264419{col 67}{space 3} 2.510755
{txt}{space 9}51  {c |}{col 14}{res}{space 2} .7142844{col 26}{space 2} .0673118{col 37}{space 1}   -3.57{col 46}{space 3}0.000{col 54}{space 4} .5936777{col 67}{space 3} .8593925
{txt}{space 9}55  {c |}{col 14}{res}{space 2}  .539307{col 26}{space 2}  .053349{col 37}{space 1}   -6.24{col 46}{space 3}0.000{col 54}{space 4} .4441429{col 67}{space 3} .6548615
{txt}{space 12} {c |}
{space 8}Year {c |}
{space 7}2006  {c |}{col 14}{res}{space 2} .9392587{col 26}{space 2} .0168496{col 37}{space 1}   -3.49{col 46}{space 3}0.001{col 54}{space 4} .9067658{col 67}{space 3} .9729159
{txt}{space 7}2007  {c |}{col 14}{res}{space 2} 1.011244{col 26}{space 2} .0186278{col 37}{space 1}    0.61{col 46}{space 3}0.544{col 54}{space 4} .9753391{col 67}{space 3} 1.048471
{txt}{space 7}2008  {c |}{col 14}{res}{space 2} 1.012567{col 26}{space 2} .0203388{col 37}{space 1}    0.62{col 46}{space 3}0.534{col 54}{space 4} .9734271{col 67}{space 3}  1.05328
{txt}{space 7}2009  {c |}{col 14}{res}{space 2} 1.047019{col 26}{space 2} .0208323{col 37}{space 1}    2.31{col 46}{space 3}0.021{col 54}{space 4} 1.006923{col 67}{space 3} 1.088713
{txt}{space 7}2010  {c |}{col 14}{res}{space 2} 1.041395{col 26}{space 2} .0217493{col 37}{space 1}    1.94{col 46}{space 3}0.052{col 54}{space 4}  .999574{col 67}{space 3} 1.084966
{txt}{space 7}2011  {c |}{col 14}{res}{space 2} 1.086019{col 26}{space 2} .0238429{col 37}{space 1}    3.76{col 46}{space 3}0.000{col 54}{space 4}  1.04022{col 67}{space 3} 1.133835
{txt}{space 12} {c |}
{space 4}1.Female {c |}{col 14}{res}{space 2} .2490626{col 26}{space 2} .0039063{col 37}{space 1}  -88.63{col 46}{space 3}0.000{col 54}{space 4} .2415131{col 67}{space 3} .2568482
{txt}std_same_p~x {c |}{col 14}{res}{space 2} .9855217{col 26}{space 2} .0088472{col 37}{space 1}   -1.62{col 46}{space 3}0.105{col 54}{space 4} .9683106{col 67}{space 3} 1.003039
{txt}{space 12} {c |}
{space 5}AgeGrp4 {c |}
{space 10}2  {c |}{col 14}{res}{space 2} 2.168537{col 26}{space 2} .0473129{col 37}{space 1}   35.48{col 46}{space 3}0.000{col 54}{space 4} 2.077643{col 67}{space 3} 2.263408
{txt}{space 10}3  {c |}{col 14}{res}{space 2} 2.432651{col 26}{space 2}  .062267{col 37}{space 1}   34.73{col 46}{space 3}0.000{col 54}{space 4} 2.313467{col 67}{space 3} 2.557975
{txt}{space 10}4  {c |}{col 14}{res}{space 2} 1.975461{col 26}{space 2}   .05505{col 37}{space 1}   24.43{col 46}{space 3}0.000{col 54}{space 4} 1.870323{col 67}{space 3} 2.086508
{txt}{space 12} {c |}
std_same_p~4 {c |}{col 14}{res}{space 2} .9666689{col 26}{space 2} .0057733{col 37}{space 1}   -5.68{col 46}{space 3}0.000{col 54}{space 4} .9554045{col 67}{space 3}  .978066
{txt}{space 12} {c |}
{space 7}Race5 {c |}
{space 10}2  {c |}{col 14}{res}{space 2} .3574671{col 26}{space 2} .0111825{col 37}{space 1}  -32.88{col 46}{space 3}0.000{col 54}{space 4}  .336181{col 67}{space 3}  .380101
{txt}{space 10}3  {c |}{col 14}{res}{space 2}  1.00948{col 26}{space 2} .0793356{col 37}{space 1}    0.12{col 46}{space 3}0.904{col 54}{space 4} .8651925{col 67}{space 3} 1.177829
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .6242554{col 26}{space 2} .0336861{col 37}{space 1}   -8.73{col 46}{space 3}0.000{col 54}{space 4} .5615248{col 67}{space 3} .6939939
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .4852734{col 26}{space 2} .0204391{col 37}{space 1}  -17.17{col 46}{space 3}0.000{col 54}{space 4} .4467737{col 67}{space 3} .5270907
{txt}{space 12} {c |}
std_same_~e5 {c |}{col 14}{res}{space 2} 1.013786{col 26}{space 2} .0102803{col 37}{space 1}    1.35{col 46}{space 3}0.177{col 54}{space 4} .9938103{col 67}{space 3} 1.034164
{txt}{space 3}1.BornUSA {c |}{col 14}{res}{space 2} 1.751992{col 26}{space 2} .0693714{col 37}{space 1}   14.16{col 46}{space 3}0.000{col 54}{space 4} 1.621002{col 67}{space 3} 1.893567
{txt}std_same_p~A {c |}{col 14}{res}{space 2} 1.024177{col 26}{space 2} .0078434{col 37}{space 1}    3.12{col 46}{space 3}0.002{col 54}{space 4} 1.008899{col 67}{space 3} 1.039686
{txt}{space 12} {c |}
{space 4}MarStat5 {c |}
{space 10}2  {c |}{col 14}{res}{space 2}  2.27768{col 26}{space 2} .0489912{col 37}{space 1}   38.27{col 46}{space 3}0.000{col 54}{space 4} 2.183533{col 67}{space 3} 2.375886
{txt}{space 10}3  {c |}{col 14}{res}{space 2} 3.093401{col 26}{space 2} .0446917{col 37}{space 1}   78.16{col 46}{space 3}0.000{col 54}{space 4} 3.006923{col 67}{space 3} 3.182366
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .9239086{col 26}{space 2} .0841834{col 37}{space 1}   -0.87{col 46}{space 3}0.385{col 54}{space 4} .7726234{col 67}{space 3} 1.104816
{txt}{space 10}5  {c |}{col 14}{res}{space 2} 2.262679{col 26}{space 2}  .040966{col 37}{space 1}   45.10{col 46}{space 3}0.000{col 54}{space 4} 2.183693{col 67}{space 3} 2.344523
{txt}{space 12} {c |}
std_same_~t5 {c |}{col 14}{res}{space 2}  .984844{col 26}{space 2}  .005941{col 37}{space 1}   -2.53{col 46}{space 3}0.012{col 54}{space 4} .9732532{col 67}{space 3} .9965728
{txt}{space 1}Rat_Poverty {c |}{col 14}{res}{space 2} 1.003457{col 26}{space 2}  .001945{col 37}{space 1}    1.78{col 46}{space 3}0.075{col 54}{space 4}  .999647{col 67}{space 3} 1.007281
{txt}{space 1}Rat_Mig_Cum {c |}{col 14}{res}{space 2}  .183706{col 26}{space 2} .0918869{col 37}{space 1}   -3.39{col 46}{space 3}0.001{col 54}{space 4}  .068834{col 67}{space 3} .4902798
{txt}{space 5}Pop_Den {c |}{col 14}{res}{space 2} .9999944{col 26}{space 2} 4.92e-06{col 37}{space 1}   -1.14{col 46}{space 3}0.255{col 54}{space 4} .9999847{col 67}{space 3} 1.000004
{txt}{space 1}Rat_GC_ProE {c |}{col 14}{res}{space 2} 1.000012{col 26}{space 2} 9.30e-06{col 37}{space 1}    1.32{col 46}{space 3}0.186{col 54}{space 4} .9999941{col 67}{space 3} 1.000031
{txt}{space 1}Rat_GC_ProM {c |}{col 14}{res}{space 2} .9999987{col 26}{space 2} .0000114{col 37}{space 1}   -0.12{col 46}{space 3}0.907{col 54}{space 4} .9999764{col 67}{space 3} 1.000021
{txt}{space 1}Rat_GC_ProB {c |}{col 14}{res}{space 2}  .999962{col 26}{space 2} .0000393{col 37}{space 1}   -0.97{col 46}{space 3}0.334{col 54}{space 4} .9998849{col 67}{space 3} 1.000039
{txt}{space 2}Rat_GC_Jew {c |}{col 14}{res}{space 2} 1.000118{col 26}{space 2} .0000664{col 37}{space 1}    1.78{col 46}{space 3}0.076{col 54}{space 4} .9999877{col 67}{space 3} 1.000248
{txt}{space 2}Rat_GC_Oth {c |}{col 14}{res}{space 2} .9998908{col 26}{space 2} .0000227{col 37}{space 1}   -4.80{col 46}{space 3}0.000{col 54}{space 4} .9998462{col 67}{space 3} .9999355
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .0000955{col 26}{space 2} .0000106{col 37}{space 1}  -83.52{col 46}{space 3}0.000{col 54}{space 4} .0000768{col 67}{space 3} .0001187
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}
{com}.         estimates store m1 
. 
.         if (`model_version' <= 4){c -(}
.                 mchange `margin_demographics', amount(all) delta(100) statistics(all) decimals(7)

{res}svy logit: Changes in Pr(y) | Number of obs = 11763920

{txt}Expression: Pr(Suic), predict(pr)
{res}
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{space 0}{hline 13}{c   +}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}
{space 0}{res:{lalign 13:Female}}{c |}{space 11}{space 11}{space 11}{space 11}{space 11}
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{space 0}{res:{lalign 13:std same p~x}}{c |}{space 11}{space 11}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:0 to 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000021}}}{space 1}{space 1}{ralign 9:{res:{sf:0.1015912}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000047}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000004}}}{space 1}{space 1}{ralign 9:{res:{sf:-1.64e+00}}}{space 1}
{space 0}{space 0}{ralign 12:+1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000021}}}{space 1}{space 1}{ralign 9:{res:{sf:0.1022936}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000047}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000004}}}{space 1}{space 1}{ralign 9:{res:{sf:-1.64e+00}}}{space 1}
{space 0}{space 0}{ralign 12:+delta}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0001131}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0002581}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0001736}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000526}}}{space 1}{space 1}{ralign 9:{res:{sf:-3.67e+00}}}{space 1}
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{space 0}{space 0}{ralign 12:Marginal}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000021}}}{space 1}{space 1}{ralign 9:{res:{sf:0.1048072}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000047}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000004}}}{space 1}{space 1}{ralign 9:{res:{sf:-1.62e+00}}}{space 1}
{space 0}{res:{lalign 13:AgeGrp4}}{c |}{space 11}{space 11}{space 11}{space 11}{space 11}
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{space 0}{space 0}{ralign 12:3 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0001088}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001034}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001142}}}{space 1}{space 1}{ralign 9:{res:{sf: 3.93e+01}}}{space 1}
{space 0}{space 0}{ralign 12:4 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000741}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000683}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000799}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.51e+01}}}{space 1}
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{space 0}{space 0}{ralign 12:4 vs 3}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000347}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000401}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000293}}}{space 1}{space 1}{ralign 9:{res:{sf:-1.26e+01}}}{space 1}
{space 0}{res:{lalign 13:std same p~4}}{c |}{space 11}{space 11}{space 11}{space 11}{space 11}
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{space 0}{space 0}{ralign 12:+delta}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0001424}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0001483}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0001365}}}{space 1}{space 1}{ralign 9:{res:{sf:-4.73e+01}}}{space 1}
{space 0}{space 0}{ralign 12:Range}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000525}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000708}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000343}}}{space 1}{space 1}{ralign 9:{res:{sf:-5.65e+00}}}{space 1}
{space 0}{space 0}{ralign 12:Marginal}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000050}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000067}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000033}}}{space 1}{space 1}{ralign 9:{res:{sf:-5.70e+00}}}{space 1}
{space 0}{res:{lalign 13:Race5}}{c |}{space 11}{space 11}{space 11}{space 11}{space 11}
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{space 0}{space 0}{ralign 12:3 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000016}}}{space 1}{space 1}{ralign 9:{res:{sf:0.9049260}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000253}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000285}}}{space 1}{space 1}{ralign 9:{res:{sf:0.1194740}}}{space 1}
{space 0}{space 0}{ralign 12:4 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000649}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000770}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000529}}}{space 1}{space 1}{ralign 9:{res:{sf:-1.06e+01}}}{space 1}
{space 0}{space 0}{ralign 12:5 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000890}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000967}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000812}}}{space 1}{space 1}{ralign 9:{res:{sf:-2.25e+01}}}{space 1}
{space 0}{space 0}{ralign 12:3 vs 2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0001127}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000846}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001407}}}{space 1}{space 1}{ralign 9:{res:{sf:7.8849425}}}{space 1}
{space 0}{space 0}{ralign 12:4 vs 2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000461}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000361}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000561}}}{space 1}{space 1}{ralign 9:{res:{sf:9.0297069}}}{space 1}
{space 0}{space 0}{ralign 12:5 vs 2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000221}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000155}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000287}}}{space 1}{space 1}{ralign 9:{res:{sf:6.5530333}}}{space 1}
{space 0}{space 0}{ralign 12:4 vs 3}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000666}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000331}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000979}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000353}}}{space 1}{space 1}{ralign 9:{res:{sf:-4.17e+00}}}{space 1}
{space 0}{space 0}{ralign 12:5 vs 3}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000906}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0001201}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000611}}}{space 1}{space 1}{ralign 9:{res:{sf:-6.03e+00}}}{space 1}
{space 0}{space 0}{ralign 12:5 vs 4}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000240}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000071}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000345}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000136}}}{space 1}{space 1}{ralign 9:{res:{sf:-4.52e+00}}}{space 1}
{space 0}{res:{lalign 13:std same p~5}}{c |}{space 11}{space 11}{space 11}{space 11}{space 11}
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{space 0}{space 0}{ralign 12:+1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000020}}}{space 1}{space 1}{ralign 9:{res:{sf:0.1804844}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000009}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000050}}}{space 1}{space 1}{ralign 9:{res:{sf:1.3402858}}}{space 1}
{space 0}{space 0}{ralign 12:+delta}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0004316}}}{space 1}{space 1}{ralign 9:{res:{sf:0.4620377}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0007196}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0015828}}}{space 1}{space 1}{ralign 9:{res:{sf:0.7358048}}}{space 1}
{space 0}{space 0}{ralign 12:Range}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000254}}}{space 1}{space 1}{ralign 9:{res:{sf:0.1923460}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000128}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000636}}}{space 1}{space 1}{ralign 9:{res:{sf:1.3046304}}}{space 1}
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{space 0}{res:{lalign 13:BornUSA}}{c |}{space 11}{space 11}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:1 vs 0}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000659}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000585}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000733}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.75e+01}}}{space 1}
{space 0}{res:{lalign 13:std same p~A}}{c |}{space 11}{space 11}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:0 to 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000036}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0024055}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000013}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000059}}}{space 1}{space 1}{ralign 9:{res:{sf:3.0434503}}}{space 1}
{space 0}{space 0}{ralign 12:+1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000036}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0020967}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000013}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000058}}}{space 1}{space 1}{ralign 9:{res:{sf:3.0849799}}}{space 1}
{space 0}{space 0}{ralign 12:+delta}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0014544}}}{space 1}{space 1}{ralign 9:{res:{sf:0.2343355}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0009441}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0038528}}}{space 1}{space 1}{ralign 9:{res:{sf:1.1900481}}}{space 1}
{space 0}{space 0}{ralign 12:Range}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000558}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0023925}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000198}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000917}}}{space 1}{space 1}{ralign 9:{res:{sf:3.0450978}}}{space 1}
{space 0}{space 0}{ralign 12:Marginal}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000035}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0018527}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000013}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000057}}}{space 1}{space 1}{ralign 9:{res:{sf:3.1219733}}}{space 1}
{space 0}{res:{lalign 13:MarStat5}}{c |}{space 11}{space 11}{space 11}{space 11}{space 11}
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{space 0}{space 0}{ralign 12:3 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0001951}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001887}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0002015}}}{space 1}{space 1}{ralign 9:{res:{sf: 6.02e+01}}}{space 1}
{space 0}{space 0}{ralign 12:4 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000071}}}{space 1}{space 1}{ralign 9:{res:{sf:0.3677647}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000225}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000084}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.9011133}}}{space 1}
{space 0}{space 0}{ralign 12:5 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0001177}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001121}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001233}}}{space 1}{space 1}{ralign 9:{res:{sf: 4.11e+01}}}{space 1}
{space 0}{space 0}{ralign 12:3 vs 2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000760}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000662}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000858}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.52e+01}}}{space 1}
{space 0}{space 0}{ralign 12:4 vs 2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0001262}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0001439}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0001085}}}{space 1}{space 1}{ralign 9:{res:{sf:-1.40e+01}}}{space 1}
{space 0}{space 0}{ralign 12:5 vs 2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000014}}}{space 1}{space 1}{ralign 9:{res:{sf:0.7931301}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000119}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000091}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.2623247}}}{space 1}
{space 0}{space 0}{ralign 12:4 vs 3}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0002022}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0002179}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0001865}}}{space 1}{space 1}{ralign 9:{res:{sf:-2.53e+01}}}{space 1}
{space 0}{space 0}{ralign 12:5 vs 3}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000774}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000857}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000691}}}{space 1}{space 1}{ralign 9:{res:{sf:-1.83e+01}}}{space 1}
{space 0}{space 0}{ralign 12:5 vs 4}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0001248}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001098}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001398}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.63e+01}}}{space 1}
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{space 0}{space 0}{ralign 12:0 to 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000022}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0110730}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000040}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000005}}}{space 1}{space 1}{ralign 9:{res:{sf:-2.55e+00}}}{space 1}
{space 0}{space 0}{ralign 12:+1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000022}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0108875}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000040}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000005}}}{space 1}{space 1}{ralign 9:{res:{sf:-2.55e+00}}}{space 1}
{space 0}{space 0}{ralign 12:+delta}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0001154}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0001533}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000775}}}{space 1}{space 1}{ralign 9:{res:{sf:-5.97e+00}}}{space 1}
{space 0}{space 0}{ralign 12:Range}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000219}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0116623}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000389}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000049}}}{space 1}{space 1}{ralign 9:{res:{sf:-2.53e+00}}}{space 1}
{space 0}{space 0}{ralign 12:Marginal}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000022}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0115050}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000040}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000005}}}{space 1}{space 1}{ralign 9:{res:{sf:-2.53e+00}}}{space 1}

{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 9:Std Err}{space 1}{space 1}{ralign 9:From}{space 1}{space 1}{ralign 9:To}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}{hline 11}
{space 0}{res:{lalign 13:Female}}{c |}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:1 vs 0}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000023}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0002436}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000607}}}{space 1}
{space 0}{res:{lalign 13:std same p~x}}{c |}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:0 to 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000013}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001471}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001449}}}{space 1}
{space 0}{space 0}{ralign 12:+1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000013}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001474}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001452}}}{space 1}
{space 0}{space 0}{ralign 12:+delta}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000308}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001474}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000343}}}{space 1}
{space 0}{space 0}{ralign 12:Range}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000096}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001551}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001394}}}{space 1}
{space 0}{space 0}{ralign 12:Marginal}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000013}}}{space 1}{space 1}{ralign 9:{res:{sf:       .z}}}{space 1}{space 1}{ralign 9:{res:{sf:       .z}}}{space 1}
{space 0}{res:{lalign 13:AgeGrp4}}{c |}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:2 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000021}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000760}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001647}}}{space 1}
{space 0}{space 0}{ralign 12:3 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000028}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000760}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001848}}}{space 1}
{space 0}{space 0}{ralign 12:4 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000030}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000760}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001501}}}{space 1}
{space 0}{space 0}{ralign 12:3 vs 2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000023}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001647}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001848}}}{space 1}
{space 0}{space 0}{ralign 12:4 vs 2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000029}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001647}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001501}}}{space 1}
{space 0}{space 0}{ralign 12:4 vs 3}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000028}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001848}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001501}}}{space 1}
{space 0}{res:{lalign 13:std same p~4}}{c |}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:0 to 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000009}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001483}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001433}}}{space 1}
{space 0}{space 0}{ralign 12:+1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000008}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001474}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001425}}}{space 1}
{space 0}{space 0}{ralign 12:+delta}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000030}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001474}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000050}}}{space 1}
{space 0}{space 0}{ralign 12:Range}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000093}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001759}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001234}}}{space 1}
{space 0}{space 0}{ralign 12:Marginal}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000009}}}{space 1}{space 1}{ralign 9:{res:{sf:       .z}}}{space 1}{space 1}{ralign 9:{res:{sf:       .z}}}{space 1}
{space 0}{res:{lalign 13:Race5}}{c |}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:2 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000027}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001728}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000618}}}{space 1}
{space 0}{space 0}{ralign 12:3 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000137}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001728}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001745}}}{space 1}
{space 0}{space 0}{ralign 12:4 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000061}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001728}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001079}}}{space 1}
{space 0}{space 0}{ralign 12:5 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000039}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001728}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000839}}}{space 1}
{space 0}{space 0}{ralign 12:3 vs 2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000143}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000618}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001745}}}{space 1}
{space 0}{space 0}{ralign 12:4 vs 2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000051}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000618}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001079}}}{space 1}
{space 0}{space 0}{ralign 12:5 vs 2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000034}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000618}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000839}}}{space 1}
{space 0}{space 0}{ralign 12:4 vs 3}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000160}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001745}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001079}}}{space 1}
{space 0}{space 0}{ralign 12:5 vs 3}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000150}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001745}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000839}}}{space 1}
{space 0}{space 0}{ralign 12:5 vs 4}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000053}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001079}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000839}}}{space 1}
{space 0}{res:{lalign 13:std same p~5}}{c |}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:0 to 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000015}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001470}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001490}}}{space 1}
{space 0}{space 0}{ralign 12:+1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000015}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001474}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001494}}}{space 1}
{space 0}{space 0}{ralign 12:+delta}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0005866}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001474}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0005790}}}{space 1}
{space 0}{space 0}{ralign 12:Range}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000195}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001400}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001654}}}{space 1}
{space 0}{space 0}{ralign 12:Marginal}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000015}}}{space 1}{space 1}{ralign 9:{res:{sf:       .z}}}{space 1}{space 1}{ralign 9:{res:{sf:       .z}}}{space 1}
{space 0}{res:{lalign 13:BornUSA}}{c |}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:1 vs 0}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000038}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000876}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001535}}}{space 1}
{space 0}{res:{lalign 13:std same p~A}}{c |}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:0 to 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000012}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001494}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001530}}}{space 1}
{space 0}{space 0}{ralign 12:+1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000012}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001474}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001509}}}{space 1}
{space 0}{space 0}{ralign 12:+delta}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0012221}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001474}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0016017}}}{space 1}
{space 0}{space 0}{ralign 12:Range}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000183}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001241}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001799}}}{space 1}
{space 0}{space 0}{ralign 12:Marginal}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000011}}}{space 1}{space 1}{ralign 9:{res:{sf:       .z}}}{space 1}{space 1}{ralign 9:{res:{sf:       .z}}}{space 1}
{space 0}{res:{lalign 13:MarStat5}}{c |}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:2 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000043}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000932}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0002123}}}{space 1}
{space 0}{space 0}{ralign 12:3 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000032}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000932}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0002884}}}{space 1}
{space 0}{space 0}{ralign 12:4 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000079}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000932}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000862}}}{space 1}
{space 0}{space 0}{ralign 12:5 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000029}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000932}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0002109}}}{space 1}
{space 0}{space 0}{ralign 12:3 vs 2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000050}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0002123}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0002884}}}{space 1}
{space 0}{space 0}{ralign 12:4 vs 2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000090}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0002123}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000862}}}{space 1}
{space 0}{space 0}{ralign 12:5 vs 2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000053}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0002123}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0002109}}}{space 1}
{space 0}{space 0}{ralign 12:4 vs 3}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000080}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0002884}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000862}}}{space 1}
{space 0}{space 0}{ralign 12:5 vs 3}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000042}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0002884}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0002109}}}{space 1}
{space 0}{space 0}{ralign 12:5 vs 4}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000076}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000862}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0002109}}}{space 1}
{space 0}{res:{lalign 13:std same p~5}}{c |}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:0 to 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000009}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001477}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001455}}}{space 1}
{space 0}{space 0}{ralign 12:+1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000009}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001474}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001451}}}{space 1}
{space 0}{space 0}{ralign 12:+delta}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000193}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001474}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000320}}}{space 1}
{space 0}{space 0}{ralign 12:Range}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000087}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001587}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001369}}}{space 1}
{space 0}{space 0}{ralign 12:Marginal}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000009}}}{space 1}{space 1}{ralign 9:{res:{sf:       .z}}}{space 1}{space 1}{ralign 9:{res:{sf:       .z}}}{space 1}
{res}
{txt}{p 0 0 2}Average predictions{p_end}

{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 9:0}{space 1}{space 1}{ralign 9:1}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:Pr(y|base)}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.9998526}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001474}}}{space 1}

{col 1}1: Delta equals 100.
{com}.         {c )-}
. 
. {c )-}
{txt}
{com}. 
. if (`model_version' == 5 | `model_version' == 6) {c -(}
.         estimates restore m1
.         margin_interact std_same_prop_Sex Female 5 `model'
.         estimates restore m1
.         margin_interact std_same_prop_AgeGrp4 AgeGrp4 5 `model'
.         estimates restore m1
.         margin_interact std_same_prop_Race5 Race5 5 `model'
.         estimates restore m1
.         margin_interact std_same_prop_BornUSA BornUSA 5 `model'
.         estimates restore m1
.         margin_interact std_same_prop_MarStat5 MarStat5 5 `model'
. 
.         if (`model_version' == 6) {c -(}
.                 estimates restore m1
.                 margin_interact std_same_prop_UnEmpl UnEmpl 5 `model'
.                 estimates restore m1
.                 margin_interact std_same_prop_PhysProb PhysProb 5 `model'
.         {c )-}
. 
. {c )-}
{txt}
{com}. 
. 
. log close 
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}/N/project/suicide_study/pnas_replication/results/log/logit_3.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res}18 Aug 2020, 08:17:33
{txt}{.-}
{smcl}
{txt}{sf}{ul off}